std::unique_ptr<OperationPass<ModuleOp>> CreateTPUResourceReadForWritePass();

// Creates a pass that reorders partitiioned resource reads and replicated
// inputs.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateTPUReorderReplicateAndPartitionedInputsPass();

// Creates a pass that partitions unpartitioned resource read/write to
// partitioned resource variables.
std::unique_ptr<OperationPass<func::FuncOp>>

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pkg net/http, func ParseSetCookie(string) (*Cookie, error) #66008
pkg net/http, method (*Request) CookiesNamed(string) []*Cookie #61472
pkg net/http, type Cookie struct, Partitioned bool #62490
pkg net/http, type Cookie struct, Quoted bool #46443
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  let summary =
    "returns `f(inputs)`, where `f`'s body is placed and partitioned.";

  let description = [{
Asynchronously executes a function, potentially across multiple devices but
within a single process. The kernel places and partitions a given function's
underlying graph, and executes each of the partitioned subgraphs as a function.
  }];

  let arguments = (ins
    Variadic<TF_Tensor>:$args,

func.func @cluster_func(%arg0: tensor<*xi32>) -> tensor<*xi32> {
  func.return %arg0 : tensor<*xi32>
}

// -----

// Tests output sharding of unpartitioned resource write takes on same sharding
// as unpartitioned resource.

// CHECK-LABEL: func @partitioned_input_output
func.func @partitioned_input_output(%arg0: tensor<!tf_type.resource<tensor<f32>>>) {

// in this function should be on the same host.
struct FunctionMetadata {
  // The original function name before partition.
  llvm::StringRef original_name;
  // The insertion point of partition functions.
  Block::iterator insertion_point;
  // The partitioned function name.
  llvm::StringRef partition_name;
  // The input values of the function.
  llvm::SmallVector<Value, 4> inputs;

// which returns a dict of string key -> EITHER:
// 1. python callable (taking a checkpoint key) returning SaveableObject OR
// 2. variable (partitioned/resource/reference or otherwise)
// https://github.com/tensorflow/tensorflow/blob/ddc1bbad3dfd4a089eb96014f26cc16664b1b2f8/tensorflow/python/training/saving/saveable_object.py#L58.

  }

  /**
   * Guides the allowed concurrency among update operations. Used as a hint for internal sizing. The
   * table is internally partitioned to try to permit the indicated number of concurrent updates
   * without contention. Because assignment of entries to these partitions is not necessarily
   * uniform, the actual concurrency observed may vary. Ideally, you should choose a value to

  }

  /**
   * Guides the allowed concurrency among update operations. Used as a hint for internal sizing. The
   * table is internally partitioned to try to permit the indicated number of concurrent updates
   * without contention. Because assignment of entries to these partitions is not necessarily
   * uniform, the actual concurrency observed may vary. Ideally, you should choose a value to

def TPUResourceReadsWritesPartitioningPass : Pass<"tf-tpu-resource-partition", "mlir::func::FuncOp"> {
  let summary = "Partitions unpartitioned resource read/write to partitioned resource variables.";

  let description = [{
    This pass creates individual resource reads/writes from the unpartitioned
    resource variable (from `tf.TPUPartitionedInput`) to individual partitioned

// RUN: tf-opt %s -split-input-file -verify-diagnostics -tf-tpu-reorder-replicate-partitioned-inputs | FileCheck %s

// CHECK-LABEL:func @simple
// CHECK-SAME: ([[ARG0:%.*]]: tensor<!tf_type.resource<tensor<10x3xf32>>>, [[ARG1:%.*]]: tensor<!tf_type.resource<tensor<10x3xf32>>>, [[ARG2:%.*]]: tensor<!tf_type.resource<tensor<10x3xf32>>>, [[ARG3:%.*]]: tensor<!tf_type.resource<tensor<10x3xf32>>>)